Faculty of Information Technology, BUT

Course details

Intelligent Systems

SIN Acad. year 2008/2009 Winter semester 5 credits

Intelligent system, intelligent systems modelling, simulation in the design of systems, uncertain and incomplete information processing, introduction to softcomputing, agent and multiagent architectures, learning and adaptive systems, reinforcement learning, planing and scheduling, applications.

Guarantor

Language of instruction

Czech

Completion

Examination (written)

Time span

26 hrs lectures, 10 hrs exercises, 2 hrs pc labs, 13 hrs projects

Assessment points

70 exam, 15 half-term test, 15 projects

Department

Lecturer

Subject specific learning outcomes and competences

Students acquire knowledge of principles and design of intelligent systems.

Learning objectives

To acquaint students with theory and principles of intelligent systems.

Prerequisite kwnowledge and skills

Artificial intelligence basics: Problem solving, state space search, problem decomposition. Modelling and Simulation basics: System, model, simulation, simulation time, discrete event simulation, continuous systems simulation.

Study literature

  1. Russel, S., Norvig, P.: Artificial Intelligence, a Modern Approach, Pearson Education Inc., 2003, ISBN 0-13-080302-2
  2. Sutton, R.S., Barto, A.G.: Reinforcement Learning - An Introduction, The MIT Press, Cambridge, MA, 1992
  3. Mitchel, T.: Machine Learning. McGraw Hill, 1997
  4. Zeigler, B.P.: Theory of Modeling and Simulation, Academic Press; 2 edition (March 15, 2000), ISBN 978-0127784557

Fundamental literature

  1. Russel, S., Norvig, P.: Artificial Intelligence, a Modern Approach, Pearson Education Inc., 2003, ISBN 0-13-080302-2
  2. Sutton, R.S., Barto, A.G.: Reinforcement Learning - An Introduction, The MIT Press, Cambridge, MA, 1992
  3. Mitchel, T.: Machine Learning. McGraw Hill, 1997
  4. Zeigler, B.P.: Theory of Modeling and Simulation, Academic Press; 2 edition (March 15, 2000), ISBN 978-0127784557

Syllabus of lectures

  1. Introduction. Intelligent systems overview
  2. Agent architectures
  3. Simulation modelling in the development of intelligent systems
  4. Fuzzy logic and fuzzy control
  5. Learning systems. Neural networks
  6. Genetic algorithms. Genetic programming
  7. Markov decision process, reinforcement learning
  8. Planing and Scheduling 
  9. Games theory
  10. Robotic systems
  11. Multiagent systems
  12. Selected applications
  13. Summary

Syllabus - others, projects and individual work of students

  • Individual project - simulation model of a simple intelligent system

Progress assessment

  • Mid-term written test
  • PC lab
  • Individuální project

Course inclusion in study plans

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